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--- |
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base_model: Spestly/Athena-1-7B |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- qwen2 |
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- trl |
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- llama-cpp |
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- gguf-my-repo |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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# Triangle104/Athena-1-7B-Q6_K-GGUF |
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This model was converted to GGUF format from [`Spestly/Athena-1-7B`](https://huggingface.co/Spestly/Athena-1-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. |
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Refer to the [original model card](https://huggingface.co/Spestly/Athena-1-7B) for more details on the model. |
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--- |
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Model details: |
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Athena-1 is a fine-tuned, instruction-following large language model derived from Qwen/Qwen2.5-7B-Instruct. |
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Designed to balance efficiency and performance, Athena 7B provides |
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powerful text-generation capabilities, making it suitable for a variety |
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of real-world applications, including conversational AI, content |
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creation, and structured data processing. |
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Key Features |
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🚀 Enhanced Performance |
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Instruction Following: Fine-tuned for excellent adherence to user prompts and instructions. |
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Coding and Mathematics: Proficient in solving coding problems and mathematical reasoning. |
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Lightweight: At 7.62 billion parameters, Athena-1-7B offers powerful performance while maintaining efficiency. |
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📖 Long-Context Understanding |
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Context Length: Supports up to 128K tokens, ensuring accurate handling of large documents or conversations. |
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Token Generation: Can generate up to 8K tokens of output. |
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🌍 Multilingual Support |
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Supports 29+ languages, including: |
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English, Chinese, French, Spanish, Portuguese, German, Italian, Russian |
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Japanese, Korean, Vietnamese, Thai, Arabic, and more. |
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📊 Structured Data & Outputs |
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Structured Data Interpretation: Understands and processes structured formats like tables and JSON. |
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Structured Output Generation: Generates well-formatted outputs, including JSON and other structured formats. |
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Model Details |
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Base Model: Qwen/Qwen2.5-7B-Instruct |
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Architecture: Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. |
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Parameters: 7.62B total (6.53B non-embedding). |
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Layers: 28 |
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Attention Heads: 28 for Q, 4 for KV. |
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Context Length: Up to 131,072 tokens. |
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Applications |
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Athena-1 is designed for a broad range of use cases: |
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Conversational AI: Create natural, human-like chatbot experiences. |
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Code Generation: Generate, debug, or explain code snippets. |
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Mathematical Problem Solving: Assist with complex calculations and reasoning. |
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Document Processing: Summarize or analyze large documents. |
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Multilingual Applications: Support for diverse languages for translation and global use cases. |
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Structured Data: Process and generate structured data, including tables and JSON. |
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Quickstart |
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Here’s how you can use Athena 7B for quick text generation: |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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messages = [ |
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{"role": "user", "content": "Who are you?"}, |
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] |
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pipe = pipeline("text-generation", model="Spestly/Athena-1-7B") |
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pipe(messages) |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-7B") |
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model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-7B") |
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--- |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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llama-cli --hf-repo Triangle104/Athena-1-7B-Q6_K-GGUF --hf-file athena-1-7b-q6_k.gguf -p "The meaning to life and the universe is" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo Triangle104/Athena-1-7B-Q6_K-GGUF --hf-file athena-1-7b-q6_k.gguf -c 2048 |
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``` |
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./llama-cli --hf-repo Triangle104/Athena-1-7B-Q6_K-GGUF --hf-file athena-1-7b-q6_k.gguf -p "The meaning to life and the universe is" |
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``` |
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or |
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``` |
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./llama-server --hf-repo Triangle104/Athena-1-7B-Q6_K-GGUF --hf-file athena-1-7b-q6_k.gguf -c 2048 |
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``` |
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